A useful AI review should tell you more than whether a product worked once. CortexLab testing is designed around repeatable tasks, relevant criteria and transparent limitations.
1. Start with the real job
We begin with the task a reader is actually trying to complete. Depending on the product, that may include research, writing, coding, image generation, data analysis, automation or a multi-step workflow.
2. Keep comparisons comparable
Where systems can reasonably be compared, we use equivalent inputs and document meaningful differences in settings, access tiers and test conditions. We avoid pretending that unlike products can be reduced to a single universal score.
3. Evaluate outcomes, not demos
We look at output quality, reliability, speed, control, usability and cost where those factors are relevant. A strong benchmark result does not automatically mean a better product experience.
4. Record limitations
AI systems are probabilistic and products change frequently. Results can vary by model version, account tier, region and time. Our articles should identify material limitations and date-sensitive conditions.
5. Update when the product changes
Material product changes may require a retest or an editorial update. We prefer a clearly dated correction or update over silently rewriting the record.